"Machine Learning is just lego for adults" - Dr. Kieran Samuel Owens
"S/he who has a why, can bear almost any how." - Friedrich Nietzche
Why do anything else, when the thing that you could do would help with everything else?
To become an expert at anything, there is a common denominator:
10,000 hours of deliberate practise on the subject.
There are 3 main features:
- PORTFOLIO
- Briefly, these are solutions to classical problems, MNIST, Boston Housing, XOR, etc.
- EDUCATION
- These contain coursework from my university and MOOCs (that which I am allowed to share). Additionally my textbook solutions are included here.
- PROFICIENCY
- These are my more complex and non-trivial projects. They are more fun, but also more novel and thus less deterministic; Kanye West chatbot, Peg Solitare Reinforcement Learner, Ultimate Frisbee Computer Vision, etc.
In no particular order, here are a list of the methods you will find in the notebooks. The emphasis is on understanding their limitations, benefits and constructions.
- Least Squares Regression
- Random Forests
- Boosting, Bagging
- Ensemble Methods
- Multilayer Perceptrons
- Naive Bayes
- K-means regression
- K-nearest Neighbours Clustering
- Logistic Regression
- Decision Trees
- SVM
- Kernel Methods
- GAN's
- Stable Diffusion
- Recurrent Neural Networks
- Convolutional Neural Networks
- Transformers
- word2vec, GLoVE and NLP
- LLM
To gain proficiency in all of the above methods, I have solved classical problems that lend themselves well to that particular method:
| Dataset | Accuracy | Model |
|---|---|---|
| MNIST | A% | KNN |
| FMNIST | B% | Random Forest |
| KMNIST | C% | 2-layer CNN |
| CIFAR | D% | CNN |
| IRIS | E% | SVM |
| ImageNet | F% | ResNet50 |
| Sentiment140 | G% | LSTM |
| Boston Housing | H% | Linear Regression |
| Wine Quality | I% | Gradient Boosting |
| Pima Indians Diabetes | J% | Decision Tree |
| IMDB Reviews | K% | BERT |
| KDD Cup 1999 | L% | K-Means Clustering |
| Digits | M% | Gaussian Mixture Model |
| CartPole | N% | Deep Q-Network |
For mastery, a formal education is also required; either by way of open-courseware, or by paying an institution.
I have done both, and overall benefitted as a result.
- UNSW AI
- UNSW Machine Learning and Data Mining
- UNSW Deep Learning and Neural Networks
- UNSW Computer Vision
- Stanford CS229 (Machine Learning)
- Stanford CS230 (Deep Learning)
- Mathematics for Machine Learning, Ong et al.
- HOML (Hands on Machine Learning)
- All of Statistics, Larry Wasserman
- Coursera Machine Learning Specialisation
- Coursera Deep Learning Specialisation
To become proficient, I have applied my ML skills to solve problems of personal and social interest.
- Kanye West chatbot
- KiTS19 Grand Challenge: Kidney and Kidney Tumour Segmentation
- Non-descriptive Ultimate Frisbee Statistics
- OCR
- Peg Solitaire RL
"Read 2 papers a week" - Andrew Ng